Dendritic and axonal morphologies play fundamental roles in physiological brain function and pathological dysfunction by affecting synaptic integration, spike train transmission, and circuit connectivity. Incorporating published experimental data into accurate, full-scale, and biologically plausible neural network simulations is important for quantitatively bridging the sub-cellular and systems-levels. We successfully designed, implemented, and freely distributed to the community computer software and databases to reconstruct, analyze, visualize, simulate, and share the 3D tree-like shape of neurons collected from any labeling and visualization techniques, animal species, brain regions, developmental stages, and experimental conditions. We imaged by light microscopy, digitally traced, and shared new data, and we provided our peers with the electronic means of freely doing the same. Moreover, we combined those data with computational models of membrane biophysics to investigate the neuronal structure-activity relationship with a special focus on the hippocampus and entorhinal cortex due to their central role in spatial representation and episodic memory. We additionally annotated a massive amount of cellular properties in an open-source web-based portal of neuron types in the rodent hippocampal formation. We now propose to expand this research approach with three specific aims. The first is to augment the power, scope, and usability of the NeuroMorpho.Org repository of digital tracings. We plan to more than double the number of shared reconstructions while enhancing the human- and machine-accessible utility by adding ?search similar? and summary reporting functionalities. Most importantly for long-term sustainability, we will dramatically modernize the information technology infrastructure of this resource to enable unsolicited submissions directly from authors, continuous agile releases, and community crowdsourcing. The second aim is to complete the Hippocampome.org knowledge base by adding synaptic information, including connection probabilities, physiology, and plasticity, and linking them to the existing morphological, physiological, and molecular properties of pre- and post-synaptic neurons. This will enable the implementation of a real-scale spiking neural network model to run predictive simulations of activity dynamics and computational functions. The third aim is to develop an innovative approach to classify neurons directly from network connectivity, validating it with the hippocampal circuit and deploying it on open-access high-throughput data from a popular model organism. Together, these three aims will allow us (and others) to test hypotheses relating neuronal morphology to molecular and developmental determinants on the one hand, and to functional circuits on the other. The focus of this application on structural plasticity is especially relevant to disabling neurological diseases prominently involving the hippocampal formation, including epilepsy and Alzheimer?s. Our proposed data-driven, biologically realistic network simulations may shed light on the role of specific neuron types and of their interactions in impairments of memory formation and retrieval.